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Amyloid β peptide-induced corpus callosum damage and glial activation in vivo

2003· article· en· W4239020096 on OpenAlexaff
Nattinee Jantaratnotai, Jae Kyu Ryu, Seung Up Kim, James G. McLarnon

Bibliographic record

VenueNeuroreport · 2003
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCorpus callosumWhite matterLuxol fast blue stainMyelinOligodendrocyteMicrogliaPathologyImmunocytochemistryBiologyAstrocyteCentral nervous systemNeuroscienceMedicineImmunologyInflammationMagnetic resonance imaging

Abstract

fetched live from OpenAlex

The effects of stereotaxic injection of amyloid β-peptide (Aβ1−42) into rat brain to induce white matter damage have been studied. Administration of 1 nmol Aβ1−42 into corpus callosum resulted in considerable damage to axons as evidenced by the loss of neurofilament-immunoreactive (NF-ir) fibers 6 h and 3 and 7 days post-injection. Significant damage was also evident to myelin (using Luxol fast blue myelin staining) and oligodendrocytes (using CC1 immunocytochemistry); in the latter case marked caspase-3 immunoreactivity was evident in oligodendrocytes. Additionally, the numbers of GFAP-ir astrocytes and OX-42/OX-6-ir microglia were markedly increased following Aβ1−42 injection. These results suggest that Aβ plays an important pathophysiological role in white matter damage and that inflammatory responses may contribute to Aβ-induced demyelination and oligodendrocyte injury in corpus callosum. Loss of function of cells in corpus callosum could provide a potential new model for the study of white matter damage in Alzheimer's disease.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.313
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations71
Published2003
Admission routes1
Has abstractyes

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